44,056 research outputs found
Stochastic multi-scale models of competition within heterogeneous cellular populations: simulation methods and mean-field analysis
We propose a modelling framework to analyse the stochastic behaviour of
heterogeneous, multi-scale cellular populations. We illustrate our methodology
with a particular example in which we study a population with an
oxygen-regulated proliferation rate. Our formulation is based on an
age-dependent stochastic process. Cells within the population are characterised
by their age. The age-dependent (oxygen-regulated) birth rate is given by a
stochastic model of oxygen-dependent cell cycle progression. We then formulate
an age-dependent birth-and-death process, which dictates the time evolution of
the cell population. The population is under a feedback loop which controls its
steady state size: cells consume oxygen which in turns fuels cell
proliferation. We show that our stochastic model of cell cycle progression
allows for heterogeneity within the cell population induced by stochastic
effects. Such heterogeneous behaviour is reflected in variations in the
proliferation rate. Within this set-up, we have established three main results.
First, we have shown that the age to the G1/S transition, which essentially
determines the birth rate, exhibits a remarkably simple scaling behaviour. This
allows for a huge simplification of our numerical methodology. A further result
is the observation that heterogeneous populations undergo an internal process
of quasi-neutral competition. Finally, we investigated the effects of
cell-cycle-phase dependent therapies (such as radiation therapy) on
heterogeneous populations. In particular, we have studied the case in which the
population contains a quiescent sub-population. Our mean-field analysis and
numerical simulations confirm that, if the survival fraction of the therapy is
too high, rescue of the quiescent population occurs. This gives rise to
emergence of resistance to therapy since the rescued population is less
sensitive to therapy
Evolution of size-dependent flowering in a variable environment: construction and analysis of a stochastic integral projection model
Understanding why individuals delay reproduction is a classic problem in evolutionary biology. In plants, the study of reproductive delays is complicated because growth and survival can be size and age dependent, individuals of the same size can grow by different amounts and there is temporal variation in the environment. We extend the recently developed integral projection approach to include size- and age-dependent demography and temporal variation. The technique is then applied to a long-term individually structured dataset for Carlina vulgaris, a monocarpic thistle. The parameterized model has excellent descriptive properties in terms of both the population size and the distributions of sizes within each age class. In Carlina, the probability of flowering depends on both plant size and age. We use the parameterized model to predict this relationship, using the evolutionarily stable strategy approach. Considering each year separately, we show that both the direction and the magnitude of selection on the flowering strategy vary from year to year. Provided the flowering strategy is constrained, so it cannot be a step function, the model accurately predicts the average size at flowering. Elasticity analysis is used to partition the size- and age-specific contributions to the stochastic growth rate, λs. We use λs to construct fitness landscapes and show how different forms of stochasticity influence its topography. We prove the existence of a unique stochastic growth rate, λs, which is independent of the initial population vector, and show that Tuljapurkar's perturbation analysis for log(λs) can be used to calculate elasticities
Kinetic theory of age-structured stochastic birth-death processes
Classical age-structured mass-action models such as the McKendrick-von Foerster equation have been extensively studied but are unable to describe stochastic fluctuations or population-size-dependent birth and death rates. Stochastic theories that treat semi-Markov age-dependent processes using, e.g., the Bellman-Harris equation do not resolve a population's age structure and are unable to quantify population-size dependencies. Conversely, current theories that include size-dependent population dynamics (e.g., mathematical models that include carrying capacity such as the logistic equation) cannot be easily extended to take into account age-dependent birth and death rates. In this paper, we present a systematic derivation of a new, fully stochastic kinetic theory for interacting age-structured populations. By defining multiparticle probability density functions, we derive a hierarchy of kinetic equations for the stochastic evolution of an aging population undergoing birth and death. We show that the fully stochastic age-dependent birth-death process precludes factorization of the corresponding probability densities, which then must be solved by using a Bogoliubov-–Born–-Green–-Kirkwood-–Yvon-like hierarchy. Explicit solutions are derived in three limits: no birth, no death, and steady state. These are then compared with their corresponding mean-field results. Our results generalize both deterministic models and existing master equation approaches by providing an intuitive and efficient way to simultaneously model age- and population-dependent stochastic dynamics applicable to the study of demography, stem cell dynamics, and disease evolution
Survival of small populations under demographic stochasticity
We estimate the mean time to extinction of small populations in an environment with constant carrying capacity but under stochastic demography. In particular, we investigate the interaction of stochastic variation in fecundity and sex ratio under several different schemes of density dependent population growth regimes. The methods used include Markov chain theory, Monte Carlo simulations, and numerical simulations based on Markov chain theory. We find a strongly enhanced extinction risk if stochasticity in sex ratio and fluctuating population size act simultaneously as compared to the case where each mechanism acts alone. The distribution of extinction times deviates slightly from a geometric one, in particular for short extinction times. We also find that whether maximization of intrinsic growth rate decreases the risk of extinction or not depends strongly on the population regulation mechanism. If the population growth regime reduces populations above the carrying capacity to a size below the carrying capacity for large r (overshooting) then the extinction risk increases if the growth rate deviates from an optimal r-value
Stochastic and deterministic models for age-structured populations with genetically variable traits
Understanding how stochastic and non-linear deterministic processes interact
is a major challenge in population dynamics theory. After a short review, we
introduce a stochastic individual-centered particle model to describe the
evolution in continuous time of a population with (continuous) age and trait
structures. The individuals reproduce asexually, age, interact and die. The
'trait' is an individual heritable property (d-dimensional vector) that may
influence birth and death rates and interactions between individuals, and vary
by mutation. In a large population limit, the random process converges to the
solution of a Gurtin-McCamy type PDE. We show that the random model has a long
time behavior that differs from its deterministic limit. However, the results
on the limiting PDE and large deviation techniques \textit{\`a la}
Freidlin-Wentzell provide estimates of the extinction time and a better
understanding of the long time behavior of the stochastic process. This has
applications to the theory of adaptive dynamics used in evolutionary biology.
We present simulations for two biological problems involving life-history trait
evolution when body size is plastic and individual growth is taken into
account.Comment: This work is a proceeding of the CANUM 2008 conferenc
A jump-growth model for predator-prey dynamics: derivation and application to marine ecosystems
This paper investigates the dynamics of biomass in a marine ecosystem. A
stochastic process is defined in which organisms undergo jumps in body size as
they catch and eat smaller organisms. Using a systematic expansion of the
master equation, we derive a deterministic equation for the macroscopic
dynamics, which we call the deterministic jump-growth equation, and a linear
Fokker-Planck equation for the stochastic fluctuations. The McKendrick--von
Foerster equation, used in previous studies, is shown to be a first-order
approximation, appropriate in equilibrium systems where predators are much
larger than their prey. The model has a power-law steady state consistent with
the approximate constancy of mass density in logarithmic intervals of body mass
often observed in marine ecosystems. The behaviours of the stochastic process,
the deterministic jump-growth equation and the McKendrick--von Foerster
equation are compared using numerical methods. The numerical analysis shows two
classes of attractors: steady states and travelling waves.Comment: 27 pages, 4 figures. Final version as published. Only minor change
Ordering dynamics in the voter model with aging
The voter model with memory-dependent dynamics is theoretically and
numerically studied at the mean-field level. The `internal age', or time an
individual spends holding the same state, is added to the set of binary states
of the population, such that the probability of changing state (or activation
probability ) depends on this age. A closed set of integro-differential
equations describing the time evolution of the fraction of individuals with a
given state and age is derived, and from it analytical results are obtained
characterizing the behavior of the system close to the absorbing states. In
general, different age-dependent activation probabilities have different
effects on the dynamics. When the activation probability is an increasing
function of the age , the system reaches a steady state with coexistence of
opinions. In the case of aging, with being a decreasing function, either
the system reaches consensus or it gets trapped in a frozen state, depending on
the value of (zero or not) and the velocity of approaching
. Moreover, when the system reaches consensus, the time ordering of
the system can be exponential () or power-law like ().
Exact conditions for having one or another behavior, together with the
equations and explicit expressions for the exponents, are provided
Toward an integrated workforce planning framework using structured equations
Strategic Workforce Planning is a company process providing best in class,
economically sound, workforce management policies and goals. Despite the
abundance of literature on the subject, this is a notorious challenge in terms
of implementation. Reasons span from the youth of the field itself to broader
data integration concerns that arise from gathering information from financial,
human resource and business excellence systems. This paper aims at setting the
first stones to a simple yet robust quantitative framework for Strategic
Workforce Planning exercises. First a method based on structured equations is
detailed. It is then used to answer two main workforce related questions: how
to optimally hire to keep labor costs flat? How to build an experience
constrained workforce at a minimal cost
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